发表机构
Instituto de Telecomunicações; Instituto Superior Técnico; University of Lisbon（电信研究所; 高等技术学院; 里斯本大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
GS-PQM是一种直接在参数域评估压缩高斯泼溅质量的全参考度量，利用支持向量回归从失真误差估计感知质量，优于25种现有度量，且计算高效。
AI 中文摘要
高斯泼溅（GS）压缩的最新进展已使GS模型大小大幅缩减。因此，可靠的目标质量评估对于比较压缩方法和指导更高效GS编解码器的开发至关重要。现有的GS质量评估通常依赖图像和视频质量度量，需要渲染预定义视点，使得质量估计依赖于所选视图。本文提出GS-PQM，一种新颖的用于训练后GS压缩的全参考质量度量，它直接在GS参数域中操作。GS-PQM使用支持向量回归模型从一组参数域失真误差中估计感知质量。实验结果表明，在评估压缩GS内容方面，GS-PQM优于25种现有的图像、视频和点云质量度量，为基于渲染的质量评估提供了一种准确且计算高效的替代方案。
英文摘要
Recent advances in Gaussian Splatting (GS) compression have enabled substantial reductions in GS model size. Reliable objective quality assessment is therefore essential for comparing compression methods and guiding the development of more efficient GS codecs. Existing GS quality assessment typically relies on image and video quality metrics, requiring rendering of predefined viewpoints and making the quality estimate dependent on the selected views. This paper introduces GS-PQM, a novel full-reference quality metric for post-training GS compression that operates directly in the GS parameter domain. GS-PQM estimates perceptual quality from a set of parameter-domain distortion errors using a Support Vector Regression model. Experimental results show that GS-PQM outperforms 25 existing image, video, and point-cloud quality metrics in assessing compressed GS content, providing an accurate and computationally efficient alternative to rendering-based quality assessment.
